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MCP Server Neurolorap

by aindreyway
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

Servidor MCP Neurolorap

Licencia: MIT Pruebas código decodificador

Servidor MCP que proporciona herramientas para el análisis y documentación de código.

Características

Herramienta de recopilación de código

  • Recopilar código de todo el proyecto

  • Recopilar código de directorios o archivos específicos

  • Recopilar código de múltiples rutas

  • Salida Markdown con resaltado de sintaxis

  • Generación de índices

  • Soporte para múltiples lenguajes de programación

Herramienta de generación de informes de estructura de proyecto

  • Analizar la estructura y las métricas del proyecto

  • Generar informes detallados en formato Markdown

  • Análisis del tamaño y complejidad de los archivos

  • Visualización basada en árboles

  • Recomendaciones para la organización del código

  • Patrones de ignorancia personalizables

Related MCP server: Code Snippet Server

Descripción rápida

# Using uvx (recommended)
uvx mcp-server-neurolorap

# Or using pip (not recommended)
pip install mcp-server-neurolorap

No necesitas instalar ni configurar ninguna dependencia manualmente. La herramienta configurará todo lo necesario para analizar y documentar el código.

Instalación

Necesitará tener UV >= 0.4.10 instalado en su máquina.

Para instalar y ejecutar el servidor:

# Install using uvx (recommended)
uvx mcp-server-neurolorap

# Or install using pip (not recommended)
pip install mcp-server-neurolorap

Esto automáticamente:

  • Instalar todas las dependencias necesarias

  • Configurar la integración de Cline

  • Configurar el servidor para uso inmediato

El servidor estará disponible mediante el protocolo MCP en Cline. Podrás usarlo para analizar y documentar el código de cualquier proyecto.

Uso

Modo de desarrollador

El servidor incluye un modo de desarrollador con interfaz de terminal JSON-RPC para interacción directa:

# Start the server in developer mode
python -m mcp_server_neurolorap --dev

Comandos disponibles:

  • help : Mostrar comandos disponibles

  • list_tools : Lista de las herramientas MCP disponibles

  • collect <path> : Recopilar código de la ruta especificada

  • report [path] : Generar informe de estructura del proyecto

  • exit : Salir del modo desarrollador

Sesión de ejemplo:

> help
Available commands:
- help: Show this help message
- list_tools: List available MCP tools
- collect <path>: Collect code from specified path
- report [path]: Generate project structure report
- exit: Exit the terminal

> list_tools
["code_collector", "project_structure_reporter"]

> collect src
Code collection complete!
Output file: code_collection.md

> report
Project structure report generated: PROJECT_STRUCTURE_REPORT.md

> exit
Goodbye!

A través de herramientas MCP

Colección de códigos

from modelcontextprotocol import use_mcp_tool

# Collect code from entire project
result = use_mcp_tool(
    "code_collector",
    {
        "input": ".",
        "title": "My Project"
    }
)

# Collect code from specific directory
result = use_mcp_tool(
    "code_collector",
    {
        "input": "./src",
        "title": "Source Code"
    }
)

# Collect code from multiple paths
result = use_mcp_tool(
    "code_collector",
    {
        "input": ["./src", "./tests"],
        "title": "Project Files"
    }
)

Análisis de la estructura del proyecto

# Generate project structure report
result = use_mcp_tool(
    "project_structure_reporter",
    {
        "output_filename": "PROJECT_STRUCTURE_REPORT.md"
    }
)

# Analyze specific directory with custom ignore patterns
result = use_mcp_tool(
    "project_structure_reporter",
    {
        "output_filename": "src_structure.md",
        "ignore_patterns": ["*.pyc", "__pycache__"]
    }
)

Almacenamiento de archivos

El servidor utiliza un enfoque estructurado para el almacenamiento de archivos:

  1. Todos los archivos generados se almacenan en ~/.mcp-docs/<project-name>/

  2. Se crea un enlace simbólico .neurolora en la raíz de su proyecto que apunta a este directorio

Esto garantiza:

  • Estructura de proyecto limpia

  • Organización de archivos consistente

  • Fácil acceso a los archivos generados

  • Soporte para múltiples proyectos

  • Sincronización confiable de archivos en diferentes entornos de SO

  • Visibilidad rápida de archivos en IDE y exploradores de archivos

Personalización de patrones de ignoración

Cree un archivo .neuroloraignore en la raíz de su proyecto para personalizar qué archivos se ignoran:

# Dependencies
node_modules/
venv/

# Build
dist/
build/

# Cache
__pycache__/
*.pyc

# IDE
.vscode/
.idea/

# Generated files
.neurolora/

Si no existe ningún archivo .neuroloraignore , se creará uno predeterminado con patrones de ignorado comunes.

Desarrollo

  1. Clonar el repositorio

  2. Crear y activar entorno virtual:

python -m venv .venv
source .venv/bin/activate  # On Unix
# or
.venv\Scripts\activate  # On Windows

  1. Instalar dependencias de desarrollo:

pip install -e ".[dev]"

  1. Ejecutar el servidor:

# Normal mode (MCP server with stdio transport)
python -m mcp_server_neurolorap

# Developer mode (JSON-RPC terminal interface)
python -m mcp_server_neurolorap --dev

Pruebas

El proyecto mantiene altos estándares de calidad a través de pruebas automatizadas e integración continua:

  • Conjunto de pruebas completo con más del 80 % de cobertura de código

  • Pruebas automatizadas en Python 3.10, 3.11 y 3.12

  • Integración continua a través de GitHub Actions

  • Análisis de seguridad periódicos y comprobaciones de dependencia

Para conocer detalles sobre el desarrollo y las pruebas, consulte PROJECT_SUMMARY.md.

Calidad del código

El proyecto mantiene altos estándares de calidad de código a través de varias herramientas:

# Format code
black .

# Sort imports
isort .

# Lint code
flake8 .

# Type check
mypy src tests

# Security check
bandit -r src/
safety check

Todas estas comprobaciones se ejecutan automáticamente en las solicitudes de extracción a través de GitHub Actions.

Canalización de CI/CD

El proyecto utiliza GitHub Actions para la integración y la implementación continuas:

  • Ejecuta pruebas en Python 3.10, 3.11 y 3.12

  • Comprueba el formato y el estilo del código

  • Realiza la verificación de tipos

  • Ejecuta análisis de seguridad

  • Genera informes de cobertura

  • Construye y valida el paquete

  • Sube artefactos de prueba

La tubería debe pasar antes de fusionar cualquier cambio.

Contribuyendo

¡Agradecemos sus contribuciones! Consulte las directrices en CONTRIBUTING.md .

Licencia

Licencia MIT. Consulte el archivo de LICENCIA para obtener más detalles.

Available Tools

2 tools
code_collectorC

Collect code from files into a markdown document

ParametersJSON Schema
NameRequiredDescriptionDefault
input_pathNo.
titleNoCode Collection
subproject_idNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic action. It does not cover critical aspects like whether this is a read-only operation, if it modifies files, error handling, performance implications, or output details. The description is insufficient for a tool with 3 parameters and an output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero wasted words. It is front-loaded with the core purpose, making it easy to parse quickly. Every word earns its place, though this conciseness comes at the cost of completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters with 0% schema coverage, an output schema, and no annotations, the description is inadequate. It does not explain parameter roles, behavioral traits, or how the output schema relates to the markdown document. The presence of an output schema reduces the need to describe return values, but other gaps remain significant.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It does not explain what 'input_path', 'title', or 'subproject_id' mean, their formats, or how they affect the collection process. The description fails to provide any semantic context beyond the tool's name.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('collect') and resource ('code from files'), specifying the output format ('into a markdown document'). It distinguishes from the sibling 'project_structure_reporter' by focusing on code content rather than structure, though the distinction could be more explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, such as the sibling 'project_structure_reporter'. It lacks context about appropriate scenarios, prerequisites, or exclusions, leaving the agent to infer usage based solely on the purpose statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

project_structure_reporterC

Generate a report of project structure metrics

ParametersJSON Schema
NameRequiredDescriptionDefault
output_filenameNoPROJECT_STRUCTURE_REPORT.md
ignore_patternsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Generate a report' implies a read-only operation that creates output, it doesn't specify whether this tool scans files, requires specific permissions, has performance implications for large projects, or what format the report takes. The description lacks important behavioral context for a tool that presumably analyzes project structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for what it communicates, though what it communicates is minimal. The structure is clear and front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there's an output schema (which should document the return format), the description doesn't need to explain return values. However, for a tool that analyzes project structure with 2 parameters and no annotations, the description is too minimal. It doesn't provide enough context about what 'project structure metrics' includes, how the tool works, or what the parameters control.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage for both parameters, the description provides no information about what 'output_filename' or 'ignore_patterns' mean or how they should be used. The description doesn't mention parameters at all, leaving the agent to guess their purpose from parameter names alone. This is inadequate for a tool with 2 parameters that have no schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Generate a report of project structure metrics' clearly states the verb ('Generate') and resource ('report of project structure metrics'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'code_collector' - both could potentially involve project analysis, so the distinction isn't explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There's no mention of when this tool is appropriate, what prerequisites might be needed, or how it differs from the sibling 'code_collector' tool. The agent must infer usage context entirely from the tool name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedcode_collector
    • First observedproject_structure_reporter
Install Server
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TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: code_collector focuses on extracting code content into a markdown document, while project_structure_reporter generates metrics about the project's structure. There is no overlap in functionality, making it easy for an agent to choose the right tool.

Naming Consistency5/5

Both tools follow a consistent noun_verb pattern (code_collector and project_structure_reporter), using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention.

Tool Count2/5

With only 2 tools, the server feels thin for a domain like project analysis or code management. This minimal set may not cover essential operations such as code analysis, dependency checking, or file manipulation, limiting its utility for broader tasks.

Completeness2/5

Inferred domain is project/code analysis, but the tool surface is severely incomplete. It lacks basic CRUD operations (e.g., no tools for creating, updating, or deleting files), code quality checks, or integration with version control, leaving significant gaps that could cause agent failures.

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